Trimming the Fat: An Insightful Exploration of Feature Selection and Dimensionality Reduction

Abstract

Feature selection and dimensionality reduction are crucial techniques in the field of data analysis and machine learning. They aim to identify and retain the most informative and relevant features while discarding redundant or noisy ones. This short review delves into the concepts, methods, and benefits of feature selection and dimensionality reduction. It explores various approaches, such as filter, wrapper, and embedded methods, as well as popular dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE. The review highlights the importance of these techniques in enhancing model performance, reducing computational complexity, and improving interpretability. By summarizing the key insights and challenges associated with feature selection and dimensionality reduction, this review aims to provide a comprehensive overview and serve as a foundation for further exploration in this field.